A pan-variant mRNA-LNP T cell vaccine protects HLA transgenic mice from mortality after infection with SARS-CoV-2 Beta
Bibliographic record
Abstract
Abstract Clinically licensed COVID-19 vaccines ameliorate viral infection by inducing vaccinee production of neutralizing antibodies that bind to the SARS-CoV-2 Spike protein to inhibit viral cellular entry (Walsh et al., 2020; Baden et al., 2021), however the clinical effectiveness of these vaccines is transitory as viral variants arise that escape antibody neutralization (Tregoning et al., 2021; Willett et al., 2022). Vaccines that solely rely upon a T cell response to combat viral infection could be transformational because they can be based on highly conserved short peptide epitopes that hold the potential for pan-variant immunity, but a mRNA-LNP T cell vaccine has not been shown to be sufficient for effective antiviral prophylaxis. Here we show that a mRNA-LNP vaccine based on highly conserved short peptide epitopes activates a CD8 + and CD4 + T cell response that prevents mortality in HLA-A*02:01 transgenic mice infected with the SARS-CoV-2 Beta variant of concern (B.1.351). In mice vaccinated with the T cell vaccine, 24% of the nucleated cells in lung were CD8 + T cells on day 7 post infection. This was 5.5 times more CD8 + T cell infiltration of the lungs in response to infection compared to the Pfizer-BioNTech Comirnaty® vaccine. Between days 2 and 7 post infection, the number of CD8 + T cells in the lung increased in mice vaccinated with the T cell vaccine and decreased in mice vaccinated with Comirnaty®. The T cell vaccine did not produce neutralizing antibodies, and thus our results demonstrate that SARS-CoV-2 viral infection can be controlled by a T cell response alone. Our results suggest that further study is merited for pan-variant T cell vaccines, and that T cell vaccines may be relevant for individuals that cannot produce neutralizing antibodies or to help mitigate Long COVID.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".